The Experts below are selected from a list of 93429 Experts worldwide ranked by ideXlab platform
Ruofei Zhang - One of the best experts on this subject based on the ideXlab platform.
-
semantic repository modeling in Image Database
International Conference on Multimedia and Expo, 2004Co-Authors: Ruofei Zhang, Zhongfei Zhang, Zhongyuan QinAbstract:This work is about content based Image Database retrieval, focusing on developing a classification based methodology to address semantics-intensive Image retrieval. With self organization map based Image feature grouping, a visual dictionary is created for color, texture, and shape feature attributes, respectively. Labeling each training Image with the keywords in the visual dictionary, a classification tree is built. Based on the statistical properties of the feature space we define a structure, called /spl alpha/-semantics graph, to discover the hidden semantic relationships among the semantic repositories embodied in the Image Database. With the /spl alpha/-semantics graph, each semantic repository is modeled as a unique fuzzy set to explicitly address the semantic uncertainty and the semantic overlap existing among the repositories in the feature space. A retrieval algorithm combining the classification tree with the fuzzy set models to deliver semantically relevant Image retrieval is provided. The experimental evaluations have demonstrated that the proposed approach models the semantic relationships effectively and outperforms a state-of-the-art content based Image retrieval system in the literature both in effectiveness and efficiency.
-
ICME - Semantic repository modeling in Image Database
2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), 1Co-Authors: Ruofei Zhang, Zhongfei Zhang, Zhongyuan QinAbstract:This work is about content based Image Database retrieval, focusing on developing a classification based methodology to address semantics-intensive Image retrieval. With self organization map based Image feature grouping, a visual dictionary is created for color, texture, and shape feature attributes, respectively. Labeling each training Image with the keywords in the visual dictionary, a classification tree is built. Based on the statistical properties of the feature space we define a structure, called /spl alpha/-semantics graph, to discover the hidden semantic relationships among the semantic repositories embodied in the Image Database. With the /spl alpha/-semantics graph, each semantic repository is modeled as a unique fuzzy set to explicitly address the semantic uncertainty and the semantic overlap existing among the repositories in the feature space. A retrieval algorithm combining the classification tree with the fuzzy set models to deliver semantically relevant Image retrieval is provided. The experimental evaluations have demonstrated that the proposed approach models the semantic relationships effectively and outperforms a state-of-the-art content based Image retrieval system in the literature both in effectiveness and efficiency.
-
ICME - Image Database Classification based on Concept Vector Model
2005 IEEE International Conference on Multimedia and Expo, 1Co-Authors: Ruofei Zhang, Zhongfei ZhangAbstract:Automatic semantic classification of Image Databases is very useful for users' searching and browsing, but it is at the same time a very challenging research problem as well. In this paper, we develop a hidden semantic concept discovery methodology to address effective semantics-intensive Image Database classification. Each Image is segmented into regions and then a uniform and sparse region-based representation is obtained. With this representation a probabilistic model based on statistical-hidden-class assumptions of the Image Database is proposed, to which the Expectation-Maximization (EM) technique is applied to analyze semantic concepts hidden in the Database. Two methods are proposed to make use of the semantic concepts discovered from the probabilistic model for unsupervised and supervised Image Database classifications, respectively, based on the automatically learned concept vectors. It is shown that the concept vectors are more reliable and robust and thus promising than the low level features through the theoretic analysis and the experimental evaluations on a Database of 10,000 general-purpose Images.
Zhongyuan Qin - One of the best experts on this subject based on the ideXlab platform.
-
semantic repository modeling in Image Database
International Conference on Multimedia and Expo, 2004Co-Authors: Ruofei Zhang, Zhongfei Zhang, Zhongyuan QinAbstract:This work is about content based Image Database retrieval, focusing on developing a classification based methodology to address semantics-intensive Image retrieval. With self organization map based Image feature grouping, a visual dictionary is created for color, texture, and shape feature attributes, respectively. Labeling each training Image with the keywords in the visual dictionary, a classification tree is built. Based on the statistical properties of the feature space we define a structure, called /spl alpha/-semantics graph, to discover the hidden semantic relationships among the semantic repositories embodied in the Image Database. With the /spl alpha/-semantics graph, each semantic repository is modeled as a unique fuzzy set to explicitly address the semantic uncertainty and the semantic overlap existing among the repositories in the feature space. A retrieval algorithm combining the classification tree with the fuzzy set models to deliver semantically relevant Image retrieval is provided. The experimental evaluations have demonstrated that the proposed approach models the semantic relationships effectively and outperforms a state-of-the-art content based Image retrieval system in the literature both in effectiveness and efficiency.
-
ICME - Semantic repository modeling in Image Database
2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), 1Co-Authors: Ruofei Zhang, Zhongfei Zhang, Zhongyuan QinAbstract:This work is about content based Image Database retrieval, focusing on developing a classification based methodology to address semantics-intensive Image retrieval. With self organization map based Image feature grouping, a visual dictionary is created for color, texture, and shape feature attributes, respectively. Labeling each training Image with the keywords in the visual dictionary, a classification tree is built. Based on the statistical properties of the feature space we define a structure, called /spl alpha/-semantics graph, to discover the hidden semantic relationships among the semantic repositories embodied in the Image Database. With the /spl alpha/-semantics graph, each semantic repository is modeled as a unique fuzzy set to explicitly address the semantic uncertainty and the semantic overlap existing among the repositories in the feature space. A retrieval algorithm combining the classification tree with the fuzzy set models to deliver semantically relevant Image retrieval is provided. The experimental evaluations have demonstrated that the proposed approach models the semantic relationships effectively and outperforms a state-of-the-art content based Image retrieval system in the literature both in effectiveness and efficiency.
Zhongfei Zhang - One of the best experts on this subject based on the ideXlab platform.
-
semantic repository modeling in Image Database
International Conference on Multimedia and Expo, 2004Co-Authors: Ruofei Zhang, Zhongfei Zhang, Zhongyuan QinAbstract:This work is about content based Image Database retrieval, focusing on developing a classification based methodology to address semantics-intensive Image retrieval. With self organization map based Image feature grouping, a visual dictionary is created for color, texture, and shape feature attributes, respectively. Labeling each training Image with the keywords in the visual dictionary, a classification tree is built. Based on the statistical properties of the feature space we define a structure, called /spl alpha/-semantics graph, to discover the hidden semantic relationships among the semantic repositories embodied in the Image Database. With the /spl alpha/-semantics graph, each semantic repository is modeled as a unique fuzzy set to explicitly address the semantic uncertainty and the semantic overlap existing among the repositories in the feature space. A retrieval algorithm combining the classification tree with the fuzzy set models to deliver semantically relevant Image retrieval is provided. The experimental evaluations have demonstrated that the proposed approach models the semantic relationships effectively and outperforms a state-of-the-art content based Image retrieval system in the literature both in effectiveness and efficiency.
-
ICME - Semantic repository modeling in Image Database
2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), 1Co-Authors: Ruofei Zhang, Zhongfei Zhang, Zhongyuan QinAbstract:This work is about content based Image Database retrieval, focusing on developing a classification based methodology to address semantics-intensive Image retrieval. With self organization map based Image feature grouping, a visual dictionary is created for color, texture, and shape feature attributes, respectively. Labeling each training Image with the keywords in the visual dictionary, a classification tree is built. Based on the statistical properties of the feature space we define a structure, called /spl alpha/-semantics graph, to discover the hidden semantic relationships among the semantic repositories embodied in the Image Database. With the /spl alpha/-semantics graph, each semantic repository is modeled as a unique fuzzy set to explicitly address the semantic uncertainty and the semantic overlap existing among the repositories in the feature space. A retrieval algorithm combining the classification tree with the fuzzy set models to deliver semantically relevant Image retrieval is provided. The experimental evaluations have demonstrated that the proposed approach models the semantic relationships effectively and outperforms a state-of-the-art content based Image retrieval system in the literature both in effectiveness and efficiency.
-
ICME - Image Database Classification based on Concept Vector Model
2005 IEEE International Conference on Multimedia and Expo, 1Co-Authors: Ruofei Zhang, Zhongfei ZhangAbstract:Automatic semantic classification of Image Databases is very useful for users' searching and browsing, but it is at the same time a very challenging research problem as well. In this paper, we develop a hidden semantic concept discovery methodology to address effective semantics-intensive Image Database classification. Each Image is segmented into regions and then a uniform and sparse region-based representation is obtained. With this representation a probabilistic model based on statistical-hidden-class assumptions of the Image Database is proposed, to which the Expectation-Maximization (EM) technique is applied to analyze semantic concepts hidden in the Database. Two methods are proposed to make use of the semantic concepts discovered from the probabilistic model for unsupervised and supervised Image Database classifications, respectively, based on the automatically learned concept vectors. It is shown that the concept vectors are more reliable and robust and thus promising than the low level features through the theoretic analysis and the experimental evaluations on a Database of 10,000 general-purpose Images.
Federica Battisti - One of the best experts on this subject based on the ideXlab platform.
-
Image Database tid2013
Signal Processing-image Communication, 2015Co-Authors: Nikolay N Ponomarenko, Lina Jin, Oleg Ieremeiev, Vladimir V Lukin, Karen Egiazarian, Jaakko Astola, Benoit Vozel, Kacem Chehdi, Marco Carli, Federica BattistiAbstract:This paper describes a recently created Image Database, TID2013, intended for evaluation of full-reference visual quality assessment metrics. With respect to TID2008, the new Database contains a larger number (3000) of test Images obtained from 25 reference Images, 24 types of distortions for each reference Image, and 5 levels for each type of distortion. Motivations for introducing 7 new types of distortions and one additional level of distortions are given; examples of distorted Images are presented. Mean opinion scores (MOS) for the new Database have been collected by performing 985 subjective experiments with volunteers (observers) from five countries (Finland, France, Italy, Ukraine, and USA). The availability of MOS allows the use of the designed Database as a fundamental tool for assessing the effectiveness of visual quality. Furthermore, existing visual quality metrics have been tested with the proposed Database and the collected results have been analyzed using rank order correlation coefficients between MOS and considered metrics. These correlation indices have been obtained both considering the full set of distorted Images and specific Image subsets, for highlighting advantages and drawbacks of existing, state of the art, quality metrics. Approaches to thorough performance analysis for a given metric are presented to detect practical situations or distortion types for which this metric is not adequate enough to human perception. The created Image Database and the collected MOS values are freely available for downloading and utilization for scientific purposes. We have created a new large Database.This Database contains larger number of distorted Images and distortion types.MOS values for all Images are obtained and provided.Analysis of correlation between MOS and a wide set of existing metrics is carried out.Methodology for determining drawbacks of existing visual quality metrics is described.
-
Image Database TID2013: Peculiarities, results and perspectives
Signal Processing: Image Communication, 2015Co-Authors: Nikolay Ponomarenko, Lina Jin, Oleg Ieremeiev, Karen Egiazarian, Jaakko Astola, Benoit Vozel, Kacem Chehdi, Marco Carli, Vladimir Lukin, Federica BattistiAbstract:This paper describes a recently created Image Database, TID2013, intended for evaluation of full-reference visual quality assessment metrics. With respect to TID2008, the new Database contains a larger number (3000) of test Images obtained from 25 reference Images, 24 types of distortions for each reference Image, and 5 levels for each type of distortion. Motivations for introducing 7 new types of distortions and one additional level of distortions are given; examples of distorted Images are presented. Mean opinion scores (MOS) for the new Database have been collected by performing 985 subjective experiments with volunteers (observers) from five countries (Finland, France, Italy, Ukraine, and USA). The availability of MOS allows the use of the designed Database as a fundamental tool for assessing the effectiveness of visual quality. Furthermore, existing visual quality metrics have been tested with the proposed Database and the collected results have been analyzed using rank order correlation coefficients between MOS and considered metrics. These correlation indices have been obtained both considering the full set of distorted Images and specific Image subsets, for highlighting advantages and drawbacks of existing, state of the art, quality metrics. Approaches to thorough performance analysis for a given metric are presented to detect practical situations or distortion types for which this metric is not adequate enough to human perception. The created Image Database and the collected MOS values are freely available for downloading and utilization for scientific purposes.
-
color Image Database for evaluation of Image quality metrics
Multimedia Signal Processing, 2008Co-Authors: Nikolay N Ponomarenko, Vladimir V Lukin, Karen Egiazarian, Jaakko Astola, Marco Carli, Federica BattistiAbstract:In this contribution, a new Image Database for testing full-reference Image quality assessment metrics is presented. It is based on 1700 test Images (25 reference Images, 17 types of distortions for each reference Image, 4 levels for each type of distortion). Using this Image Database, 654 observers from three different countries (Finland, Italy, and Ukraine) have carried out about 400000 individual human quality judgments (more than 200 judgments for each distorted Image). The obtained mean opinion scores for the considered Images can be used for evaluating the performances of visual quality metrics as well as for comparison and for the design of new metrics. The Database, with testing results, is freely available.
Tok Wang Ling - One of the best experts on this subject based on the ideXlab platform.
-
Indexing iconic Image Database for interactive spatial similarity retrieval
Lecture Notes in Computer Science, 2004Co-Authors: Xiao Ming Zhou, Chuan-heng Ang, Tok Wang LingAbstract:Similarity-based retrieval of Images is an important task in many Image Database applications. Interactive similarity retrieval is one way to resolve the fuzzy area involving psychological and physiological factors of individuals during the retrieval process. A good interactive similarity system is not only dependent on a good measure system, but also closely related to the structure of the Image Database and the retrieval process based on the respective Image Database structure. In this paper, we propose to use a digraph of most similar Image as an index structure of an iconic spatial similarity retrieval. Our approach makes use of the simple feedback from the user, and avoids the high cost of recomputation of interactive retrieval algorithm. The interactive similarity retrieval process is similar to a guided navigation by the system measure and the user in the Image Database. The proposed approach prevents looping and guarantees to find the target Image. It is straightforward and adaptive to different similarity measure.
